This is a fine-tuned version of google/functiongemma-270m-it optimized for reliable function calling. It was trained as part of the "Tiny Agent Lab" project to distill the capabilities of larger models into a highly efficient 270M parameter model.

Model Description

Model Type: Causal LM (Gemma)
Language(s): English
License: Gemma Terms of Use
Finetuned from: google/functiongemma-270m-it

Capabilities

This model is designed to:

Detect User Intent: Accurately identify when a tool call is needed.
Generate Function Calls: Output valid <start_function_call> XML/JSON blocks.
Refuse Out-of-Scope Requests: Politely decline requests for which no tool is available.
Ask Clarification: Request missing parameter values interactively.

Performance (V4 Evaluation)

On a held-out test set of 100 diverse queries:

Overall Accuracy: 71%
Tool Selection Precision: 88%
Tool Selection Recall: 94%
F1 Score: 0.91

Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "CuriousDragon/functiongemma-270m-tiny-agent"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16)

# ... (Add your inference code here)

Intended Use

This model is intended for research and educational purposes in building efficient agentic systems. It works best when provided with a clear system prompt defining the available tools.